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JACIII Vol.26 No.1 pp. 83-87
doi: 10.20965/jaciii.2022.p0083
(2022)

Paper:

Target Detection Based on Variable Frame Rate Sampling of Active Light Source

Shanshan Yuan and Xiangyang Xu

School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 10081, China

Corresponding author

Received:
April 19, 2021
Accepted:
November 15, 2021
Published:
January 20, 2022
Keywords:
target detection, image modulation, variable frame rate sampling
Abstract

In the process of target detection with active light sources as calibration objects, air scattering and air absorption cause a significant loss of light energy, resulting in distortion and fragmentation of the spot shape. Inspired by band-pass filtering, this study proposes a target detection method based on variable frame rate sampling of an active light source. It primarily adopts i) image modulation for collecting the active light source signal with a specified frequency and subtracting the background, and ii) variable frame rate sampling for further weighted average to attenuate the dynamic noise. The experimental results show that the proposed method can efficiently eliminate static background, suppress dynamic noise, and detect the target location without illumination and background requirements.

Cite this article as:
S. Yuan and X. Xu, “Target Detection Based on Variable Frame Rate Sampling of Active Light Source,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.1, pp. 83-87, 2022.
Data files:
References
  1. [1] Z. Wang, X. Wang, and J. Sun, “Current status of laser applications and its developing trend,” Laser Technology & Applications, Vol.24, No.8, pp. 31-37, 2007 (in Chinese).
  2. [2] Q. You, C. Zhang, and J. Qiu, “Laser spot center detection algorithm in complex background,” Intelligent Computer and Applications, Vol.10, No.4, pp. 124-128, 2020 (in Chinese).
  3. [3] T. Fu, S. Wang, C. Li, and S. Jin, “Design and realization of loading and unloading manipulator for laser marking machine,” Experimental Technology and Management, Vol.38, No.3, pp. 103-106, 2021 (in Chinese).
  4. [4] X. Wang, S. Wang, D. Chen, and J. Zhao, “Design of laser tracking system with quadrant detector,” Laser & Infrared, Vol.47, No.4, pp. 432-436, 2017 (in Chinese).
  5. [5] S. Li and Y. Zhang, “Annular facula detection and error compensation of four-quadrant photoelectric detection in space laser communication,” Chinese J. of Laser, Vol.44, No.11, pp. 190-201, 2017 (in Chinese).
  6. [6] Z. Li, Y. Gong, and S. Zhu, “Infrared wireless rangefinder based on MCU,” Electronics World, Vol.15, pp. 183-184, 2019 (in Chinese).
  7. [7] N. Sun, Y. Qiao, and W. Lin, “Noncontact position measuring system with planar array CCD,” J. of Changchun University of Science and Technology, Vol.25, No.4, pp. 26-28, 2002 (in Chinese).
  8. [8] J. Bromage, S.-W. Bahk, D. Irwin et al., “A focal-spot diagnostic for on-shot characterization of high-energy petawatt lasers,” Optics Express, Vol.16, No.21, pp. 16561-16572, 2008.
  9. [9] K. S. Selvanayaki and R. M. Somasundaram, “A survey on image segmentation techniques for edge detection,” Int. Conf. on Innovation in Communication, Information and Computing, Vol.303, No.2, pp. 31-34, 2013.
  10. [10] B.-H. Do and S.-C. Huang, “Dynamic background modeling based on radial basis function neural networks for moving object detection,” 2011 IEEE Int. Conf. on Multimedia and Expo, doi: 10.1109/ICME.2011.6012085, 2011.
  11. [11] M. Piccardi, “Background subtraction techniques: A review,” 2004 IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 3099-3104, 2004.
  12. [12] M. Cristani, M. Farenzena, D. Bloisi et al., “Background subtraction for automated multisensor surveillance: A comprehensive review,” EURASIP J. on Advances in Signal Processing, Vol.2010, Article No.343057, 2010.
  13. [13] T. Bouwmans, “Recent advanced statistical background modeling for foreground detection – A systematic survey,” Recent Patents on Computer Science, Vol.4, No.3, pp. 147-176, 2011.
  14. [14] T. Bouwmans, “Traditional and recent approaches in background modeling for foreground detection: An overview,” Computer Science Review, Vol.11, pp. 31-66, 2014.
  15. [15] L. Hou, Q. Liu, Z. Chen, and J. Xu, “Human detection in intelligent video surveillance: A review,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.7, pp. 1056-1064, doi: 10.20965/jaciii.2018.p1056, 2018.
  16. [16] Y. Zhang and Y. Liu, “Moving object detection based on method of frame difference and background subtraction,” Computer Technology and Development, Vol.27, No.02, pp. 25-28, 2017 (in Chinese).
  17. [17] L. Song, W. Wu, J. Guo, and X. Li, “Research on sub-pixel location of the laser spot center,” 2013 5th Int. Conf. on Intelligent Human-Machine Systems and Cybernetics, pp. 378-381, 2013.
  18. [18] K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Background modeling and subtraction by codebook construction,” 2004 Int. Conf. on Image Processing, pp. 3061-3064, 2004.
  19. [19] Y. Xu, X. Xu, and R. Ru, “Disparity optimization algorithm for stereo matching using improved guided filter,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.4, pp. 625-633, doi: 10.20965/jaciii.2019.p0625, 2019.
  20. [20] H. Wang, H. Li, and W. Du, “An adaptive multi band-pass filter algorithm and its application in fault diagnosis of rolling bearing,” J. of Vibroengineering, Vol.23, No.2, pp. 347-359, 2021.

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